Manager, Data Science

• Lead a team of marketing data scientists in defining and executing end-to-end data science roadmaps that support lifecycle marketing across QuickBooks Services (Capital, Payroll, Payments, Bill Pay) and Mailchimp, aligned to short- and long-term business outcomes
• Own the measurement, experimentation, and modeling strategy for lifecycle marketing initiatives, including onboarding, attach, upsell, retention, and active use, leveraging causal inference, experimentation frameworks, and advanced analytics
• Design, evaluate, and scale incrementality measurement approaches, including randomized experiments, holdouts, and quasi-experimental methods, to quantify the true impact of lifecycle marketing across Email, IPD, Push, SMS, Web, and cross-channel programs
• Drive development and adoption of machine learning models for lifecycle marketing use cases such as propensity scoring, churn risk, personalization, and next-best-action, in partnership with central DS and ML platform teams
• Translate complex analytical findings into clear, data-backed perspectives on marketing and business performance, with actionable recommendations tied to customer growth, revenue, and retention
• Partner closely with Lifecycle Marketing, CRM Analytics, Product, GTM, and Finance to ensure strong metric definitions, data quality, and alignment between marketing performance and financial outcomes
• Shape forward-looking data science capabilities by identifying gaps in experimentation, modeling, and data infrastructure, and influencing investments that improve learning velocity and decision-making
• Manage a team of data scientists and contractors, including coaching on technical rigor, experimental design, modeling best practices, and data storytelling, while owning prioritization, intake, and delivery

• 7+ years of experience applying data science, advanced analytics, or quantitative methods to marketing, growth, or lifecycle use cases
• Experience leading and developing teams of data analysts or data scientists, with a demonstrated ability to coach both technical and business skills
• Strong expertise in statistics, experimental design, and causal inference, including A/B testing, multivariate testing, and incremental lift measurement
• Hands-on experience building or operationalizing machine learning models (e.g., propensity, segmentation, churn, personalization) in partnership with engineering or platform teams
• Proficiency with SQL and Python (or equivalent) for data analysis, experimentation, and modeling
• Proven ability to lead cross-functional analytical projects end-to-end, from problem framing through execution and executive readout
• Strong data storytelling and influence skills, with experience presenting insights and recommendations to senior leaders
• Domain experience in lifecycle marketing, CRM, fintech, SaaS, or marketing technology preferred

Domain experience in lifecycle marketing, CRM, fintech, SaaS, or marketing technology